Method for compressing the dynamic range of digital projection radiographic images

ABSTRACT

A method for compressing the dynamic range of a digital radiographic image comprising the steps of: providing an original digital radiographic image; processing the original digital radiographic image with an unsharp masking technique to produce a low frequency digital radiographic image having low frequency components; subtracting the low frequency digital radiographic image from the original digital radiographic image to produce a high frequency digital radiographic image; multiplying the low frequency image by a first gain factor and the high frequency image by a second gain factor to produce respective modified low and high frequency images; combining the modified low and high frequency images with a mid-range image to produce an output digital image; and passing the output digital image through a look-up table to render an image with code values that represent optical density of a printed or displayed rendering of the image.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part application of U.S. patentapplication Ser. No. 08/804,998, filed Feb. 25, 1997, inventors Oliyide,VanMetter et al., now U.S. Pat. No. 5,978,518.

FIELD OF INVENTION

The present invention relates in general to image enhancement in digitalimage processing and in particular, to a method of image enhancement tobe used in a medical radiographic imaging system, such as a computedradiography system.

BACKGROUND OF THE INVENTION

It is a common desire to enhance images acquired from imaging devicessuch as medical diagnostic (radiographic) imaging devices. Suchenhancement may involve amplifying detail or local contrast in regionsof interest, increasing or decreasing global contrast and sharpeningedges. Enhancement algorithms, however, typically have the undesirableeffect of enhancing and amplifying noise. It is therefore beneficial toprovide a system for enhancing images while suppressing noiseamplification or even reducing noise.

U.S. Pat. No. 5,461,655, by P. Vuylsteke and P. Dewaele, entitled"Method and Apparatus For Noise Reduction," describes a multiresolutionnoise reduction method that includes dynamic range compression. Thedisclosed method has the following features.

Only noise suppression and contrast modification are addressed. There isno mention of how to increase the amplitude of detail images whilesuppressing noise in order to yield sharper images. It is desirable toenhance images while controlling noise.

The multiresolution representation involves a hierarchy of differenceimages, where the image size decreases at coarser scales. It isdesirable to minimize the cost and time involved in subsampling andinterpolating.

A means for including anatomical information in the processing is notprovided, whereas it is desirable to consider anatomical informationwhen determining the amount of enhancement to perform.

The noise estimation involves deriving a single quantity for noise in adetail image. Because noise varies across regions of the image, and withrespect to code value, it is desirable to provide more reduction whichadapts to spatially or code value varying noise.

Determining local image content is a separate step from noiseestimation. It is desirable to consider local image content in the noiseestimation.

Dynamic range compression occurs in serial with noise suppression. It isdesirable to modify dynamic range, enhance detail and suppressed noisein parallel, allowing for more efficient operation.

The following patents are also relevant to the present invention.

U.S. Pat. No. 5,363,209, by R. Eschbach and W. A. Fuss, titled"Image-Dependent Sharpness Enhancement".

U.S. Pat. No. 4,941,190, by T. Joyce, titled "Method and System ForEnhancement of a Digitized Image".

U.S. Pat. No. 4,827,528, by A. Macovski, titled "Error-Minimizing NoiseReduction System". In the disclosed method however, the low frequencyimage is unmodified.

A paper titled "Automatic Anatomically Selective Image Enhancement inDigital Chest Radiography," published in IEEE Transactions on MedicalImaging, volume 8, number 2, June 1989, by Sezan, Tekalp and Schaetzingdescribes a method of deriving an anatomically based lookup table.

Projection radiographic images can be acquired by several methods,including computed radiography (such as the KODAK DIGITAL SCIENCEComputed Radiography System 400) and direct digital radiography (such asthe TFT flat panel systems now under development). In any case, theresulting digital code-value data represents a mathematicaltransformation of the x-ray exposure incident upon the imaging detector(for example the logarithm of the exposure). An appropriate look-uptable is needed to select a limited range of these code values into codevalues that can be used to generate a film image by means of a laserprinter or to view the image on a display device (such as a workstationCRT). The best look-up table is often a compromise between one withadequate latitude that renders the required range of exposures and onethat gives adequate contrast for visualizing features within the image.

None of the techniques disclosed in these patents provide a satisfactorysolution to image enhancement in medical image processing.

SUMMARY OF THE INVENTION

According to the present invention there is provided a method of digitalimage enhancement, especially image enhancement of medical diagnostic(radiographic) digital images.

According to a feature of the present invention, there is provided amethod for compressing the dynamic range of a digital radiographic imagecomprising the steps of: providing an original digital radiographicimage; processing the original digital radiographic image with anunsharp masking technique to produce a low frequency digitalradiographic image having low frequency components; subtracting the lowfrequency digital radiographic image from the original digitalradiographic image to produce a high frequency digital radiographicimage; multiplying the low frequency image by a first gain factor andthe high frequency image by a second gain factor to produce respectivemodified low and high frequency images; combining the modified low andhigh frequency images with a mid-range image to produce an outputdigital image; and passing the output digital image through a look-uptable to render an image with code values that represent optical densityof a printed or displayed rendering of the image.

ADVANTAGEOUS EFFECT OF THE INVENTION

It is a principal object of the present invention to provide a means forenhancing an image.

DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagrammatic view of the method of the present invention.

FIG. 2 is a block diagram of an imaging system incorporating the presentinvention.

FIG. 3 is a diagrammatic view of an example of a 3 level decompositionstep used in the method of the present invention.

FIGS. 4a-4d are graphical views showing examples of dynamic rangemodification.

FIGS. 5a-5b are graphical views of linear and nonlinear dynamic rangecompression.

FIGS. 6a-6c are graphical views of enhancement functions.

FIG. 7 is a block diagram of an image enhancement system for carryingout the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The dynamic range compression described below provides a method ofmaintaining adequate contrast for features within the image whileincreasing the latitude (range of exposures) that are rendered in theimage. This avoids the expected trade-off of contrast for latitude andresults in an improved image.

Unsharp masking is applied to the exposure code value image data asfollows. The exposure code value data, E(i,j) is convolved with a kernelwhose dimension is more than 1/40th, but less than 1/10th the size ofthe short dimension of the image matrix. The resulting image E_(low)(i,j) is an unsharp version of the original. The functional output ofthe dynamic range compression is given by the modified exposure codevalues given by:

    E'(i,j)=α·E.sub.low (i,j)+(1-α)E.sub.mid +β·E.sub.high (i,j)

where

    E.sub.high (i,j)=E(i,j)-E.sub.low (i,j),

E_(mid) is the exposure code value corresponding to the density in theimage that will remain invariant when the dynamic range compression isapplied (E_(mid) is image dependent), α is the low-frequency gain, and βis the high-frequency gain. The expected range for α is greater thanzero and less than one. The expected range for β is greater than one.

The modified exposure code values are then passed through a look-uptable to render code values that represent optical density of a printedor displayed rendering of the image.

A digital image in which code value is linearly related to log exposureis captured with an image acquisition unit. A diagram of anotherenhancement technique is shown in FIG. 1. The log exposure data is thenprocessed by the algorithm. In FIG. 1, the data is split into twofrequency bands, or two resolution levels. The first resolution level iscreated by lowpass filtering the data by LPF 10. The second resolutionlevel is created by subtracting the lowpass image from the original atsubtractor 12, which isolates the high frequencies of the image tocreate a highpass image. The lowpass image is then spatially multipliedby a constant number and shifted to reflect the desired compression bycircuit 14. The highpass data is processed by a set of enhancementtables in circuit 16. In FIG. 1, the top enhancement table of circuit 16represents the output of a noise estimation algorithm. In radiography,high code values typically contain less noise than low code values, sothe high code values in the present invention receive the greatestamount of amplification. The result is a greater amount of sharpening inregions that contain more exposure than regions with low amounts ofexposure. The middle table represents equal amounts of sharpening in thewhole image. The bottom table represents amplification of the highfrequencies based on anatomical information. The processed low and highfrequencies are then combined in adder 18. The resulting image is thenshifted by shifter 20, for example, to match the mean of the incomingimage. A lookup table 22 is then applied to the processed image to yielddensities for final printing.

FIG. 2 shows an example of the use of the enhancement system An imageacquired from an image acquisition unit 30 is processed by an imageenhancement system incorporating the present invention, and is thendisplayed 32 on, for example, film or a display monitor 34.

FIG. 3 shows an example of a multiresolution decomposition system. Thesystem includes LPFs 40,42, subtractors 44 and 46, and adder 48. Theimage sizes of the resolution images are equal to the image size of theoriginal input image, although this need not be the case. The lowpassfilters 40,42 can be chosen to meet some desired objective, such asseparating noise from edges. In our present system, the filter kernelsare chosen to be square arrays of ones. If no processing is performed,the output image is exactly the input image.

FIG. 4 shows some uses of dynamic range compression and expansion. Thedynamic range modification can be linear or non linear. Linear dynamicrange modification is performed by multiplying the lowpass image by aconstant, and then shifting the result to the mean of the originalimage. The shifting can also be done to vary the zero-crossing of thedynamic range compression and to shift the brightness of the displayedimage to a satisfying level. In FIGS. 4a-4b, the dynamic range of theoriginal is compressed by multiplying the data by a constant less thanunity and then shifting the result so that its mean is equal to the meanof the original data. The effect is to enable data at the two extremesthat otherwise would have gotten clipped to the same density outputlevel by the tonescale to be mapped by the linear portion of the curve.By mapping the two extremes to the linear portion of the curve, thelocal contrast in some regions can increase. In FIGS. 4c-4d, the dynamicrange is expanded by multiplying the data by a constant greater thanunity and shifting the resulting data so that its mean is equal to themean of the original data. The effect is to increase the global contrastof the displayed image.

FIGS. 5a-5b shows some examples of curves that can be used for dynamicrange compression. The top curve (FIG. 5a) is linear compression, andthe bottom curve (FIG. 5b) is nonlinear compression.

FIGS. 6a-6c shows the curves used to sharpen the images. The top curve(FIG. 6a) represents an enhancement curve based on a code value-basednoise estimation algorithm. The algorithm is described further bySnyder. The curve is generated as follows:

1. The data range is partitioned into sub-intervals of equal length;

2. Edge detection is performed on the data, yielding an array ofgradient values;

3. Gradient histograms are computed for each sub-interval;

4. The gradient histograms are thresholded to differentiate gradientsassociated with noise from gradients associated with edges;

5. If a pixel is determined to not be an edge, its value is averagedwith surrounding pixel values to determine an approximate noise freevalue;

6. If a pixel is determined to not be an edge, a noise measure isdetermined by determining the magnitude of the difference between thepixel and its approximate noise free value. For spatial noiseestimation, these noise measures are sufficient to determine appropriateamounts of enhancement;

7. The standard deviation of the noise measures in each subinterval arecomputed;

8. The standard deviations are used generate the parameters to anexponential model of the noise distribution.

From the exponential curve generated by the noise estimation algorithm,appropriate weights to the high frequency images are generated.

The middle curve (FIG. 6b) demonstrates the profile of enhancement forregular unsharp masking. The bottom curve (FIG. 6c) represents a curvegenerated for anatomically selective enhancement. The curve wasgenerated by the algorithm described by Sezan.

The digital image is processed in image enhancement system 32 (FIG. 2)according to the method of the present invention. System 32 can take theform of a digital computer, such as illustrated in FIG. 7. In such case,one or more of the steps of said method can be carried out usingsoftware routines. Image processor can also include hardware or firmwarefor carrying out one or more of the method steps. Thus, the steps of themethod of the invention can be carried out using software, firmware, andhardware, either alone or in any preferable combination.

As shown in FIG. 7, a digital computer 300 includes a memory 310 forstoring digital images, application programs, operating system, etc.Memory 310 can include mass memory (such as a hard magnetic disc or CDROM), and fast memory (such as RAM). Computer 300 also includes inputdevice 312 (such as a keyboard, mouse, touch screen), display 314 (CRTmonitor, LCD), central processing unit 316 (microprocessor), outputdevice 318 (thermal printer, dot matrix printer, laser printer, ink jetprinter). Components 310, 312, 314, 316, 1n3 318 are connected togetherby control/data bus 320. Computer 300 can include a transportablestorage medium drive 322 for reading from and/or writing totransportable storage media 324, such as a floppy magnetic disk orwriteable optical compact disk (CD).

As used in this application, computer readable storage medium caninclude, specifically, memory 310 and transportable storage medium 324.More generally, computer storage medium may comprise, for example,magnetic storage media, such as magnetic disk (hard drive, floppy disk)or magnetic tape; optical storage media, such as optical disk, opticaltape, or machine readable bar code; solid state electronic storagedevices, such as random access memory (RAM), read only memory (ROM); orany other physical device or medium which can be employed to store acomputer program.

The utility of the enhancement algorithm has been demonstrated.Noise-estimation based enhancement was tested by generating a series ofenhanced images with a certain set of parameters and comparing them toimages processed with a regular unsharp masking program with acomparable set of parameters. The images from the noise estimation basedenhancement algorithm were clearly less noisy than the images from theregular algorithm, although the sharpness of the edges were equal. Theanatomically selective enhancement algorithm is currently in a Kodakproduct, and has repeatedly been demonstrated to work. The utility ofthe present invention was further tested by generating a series ofimages with the method and asking experienced observers to evaluatethem. The response to date has been positive.

Parts List

10 LPF

12 subtractor

14,16 circuit

18 adder

20 shifter

22 look-up-table

30 image acquisition unit

32 image enhancement system

34 display monitor

40,42 LPF

44,46 subtractors

48 adder

300 digital computer

310 memory

312 input device

314 display

316 central processing unit

318 output device

320 control/data bus

322 transportable storage medium drive

324 transportable storage media

What is claimed is:
 1. A method for compressing the dynamic range of adigital radiographic image comprising the steps of:providing an originaldigital radiographic image; processing said original digitalradiographic image with an unsharp masking technique to produce a lowfrequency digital radiographic image having low frequency components;subtracting said low frequency digital radiographic image from saidoriginal digital radiographic image to produce a high frequency digitalradiographic image; multiplying said low frequency image by a first gainfactor and said high frequency image by a second gain factor to producerespective modified low and high frequency images; combining saidmodified low and high frequency images with a mid-range image to producean output digital image; and passing said output digital image through alook-up table to render an image with code values that represent opticaldensity of a printed or displayed rendering of the image.
 2. The methodof claim 1 wherein said providing step provides a projection digitalradiographic image produced by computed radiography or direct digitalradiography.
 3. The method of claim 1 wherein said processing stepincludes convolving the exposure code value data E(i,j) of said originalimage with a kernel whose dimension is more than 1/40th but less than1/10th, the size of the short dimension of the image matrix to producean image E_(low) (i,j) which is an unsharp version of said originalimage.
 4. The method of claim 3 wherein said subtracting step isexpressed as:

    E.sub.high (i,j)=E(i,j)-E.sub.low (i,j).


5. The method of claim 4 wherein in said multiplying step said firstgain factor is greater than 0 but less than 1, said second gain factor βis greater than 1, and in said combining step,

    E'(i,j)=αE.sub.low (i,j)+(1-α)E.sub.mid +βE.sub.high (i,j)

where E'(i,j) is the output digital image; and Emid is the exposure codevalue corresponding to the density in the image that remains invariant.6. A computer storage product comprising:a digital storage devicecontaining data to be read and used by a computer, said data including acomputer program for carrying out a method for compressing the dynamicrange of a digital radiographic image comprising the steps of:providingan original digital radiographic image; processing said original digitalradiographic image with an unsharp masking technique to produce a lowfrequency digital radiographic image having low frequency components;subtracting said low frequency digital radiographic image from saidoriginal digital radiographic image to produce a high frequency digitalradiographic image; multiplying said low frequency image by a first gainfactor and said high frequency image by a second gain factor to producerespective modified low and high frequency images; combining saidmodified low and high frequency images with a mid-range image to producean output digital image; and passing said output digital image through alook-up table to render an image with code values that represent opticaldensity of a printed or displayed rendering of the image.